Name
Abstract | ||
|---|---|---|
We study the functional relationship between quantum control pulses in the idealized case and the pulses in the presence of an unwanted drift. We show that a class of artificial neural networks called LSTM is able to model this functional relationship with high efficiency, and hence the correction scheme required to counterbalance the effect of the drift. Our solution allows studying the mapping from quantum control pulses to system dynamics and analysing its behaviour with respect to the local variations in the control profile. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11128-019-2240-7 | Quantum Information Processing |
Keywords | Field | DocType |
Quantum dynamics, Quantum control, Deep learning, Recurrent neural network | Topology,Quantum mechanics,Quantum control,Recurrent neural network,System dynamics,Artificial intelligence,Deep learning,Artificial neural network,Deep neural networks,Quantum dynamics,Physics | Journal |
Volume | Issue | ISSN |
18 | 5 | 1570-0755 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mateusz Ostaszewski | 1 | 3 | 2.15 |
| Jaroslaw Adam Miszczak | 2 | 9 | 9.43 |
| Przemyslaw Sadowski | 3 | 4 | 1.99 |
| L. Banchi | 4 | 0 | 0.34 |